What needs to be improved about this repo:

Generalize and standardize the steps in an NLP pipeline into python classes and
functions. I can think of these off the top of my head:

* Scraper - get text from the internet to local file
* Cleaner - clean raw text of non-corpus text
* Ngramer - assemble text in python list of lists
* Cfdister - restructure data into a conditional frequency distribution
* Other? - restructure data by other metric (rhyming, similarity, etc.)
* Assembler loop - takes structure above and outputs one word
    - Maybe should wrap in a sentence loop, line-by-line loop, paragraph loop,
      etc.

Syntax aware generate is actually pretty bad. I think it forces it to be too
random. The POS tagging is too error prone and fine-detailed.

Ideas for the future:

Pick one or two lines of the haiku from actual haiku or other poems. Then add a
line or two from the corpus (e.g. trump tweets) that both fits the syllables and
rhymes with the end(s) of the real poetic line. I think both sources could be
ngram generated, but I think it would be ideal if they were picked wholesale
from the source. The problem with that approach is that you'd also have to find
a common word between the two source extractions so that the sentence doesn't
abruptly shift between lines. Or, maybe that's a good thing? I guess I should
try both.

Maybe try just switching out the nouns, verbs, adjectives, and adverbs leaving
the rest of the sentence structure largely intact after the tree replace?